Assurance of Information Systems' Quality and Security

Currently, all areas of human activity are related to information systems, so the current problems in the use of information systems are currently reliable protection of information from cyber threats and malware and quality assurance of information systems. The amount of information is constantly growing, so the issue of information security is becoming more acute. The need for quality and safety is based on the fact that errors and failures in the information systems, the impact of malware threaten disasters that lead to human casualties, environmental cataclysms, significant time losses and financial damage, or at least reputational damage to the company. Known methods and tools in the field of cybersecurity and quality assurance are unable to provide reliable protection of information from malware, detection and disposal of malware, as well as unable to ensure the required level of quality of information systems. Therefore, special attention in the direction of development and implementation of effective information systems is currently needed in the field of quality and security of information systems. Achieving high quality information systems, as well as their cybersecurity is a key factor in their effective use and one of the main needs of customers. This Special Issue aims to disseminate and discuss the quality and security of information systems. There are 8 original science-intensive studies in our Special Issue on the following topics: • Information Systems’ Quality • Information Systems’ Security • Information Systems’ Reliability • Cybersecurity in Information Systems’ Domain Some of it was accepted after a careful review and published in the proposed special issue

Method of Increasing the Resolution of the Images Based on Artificial Neural Networks

This paper presents a new method for increasing the resolution of the image - based on the artificial neural networks. The advantage of the developed ANN's method based on models of geometric transformation is to achieve a high level of generalization in a limited sample of training data. A series of model experiments to establish optimal parameters for image preprocessing and ANN training are made. Experiment on the decomposition of the image to frames for to form learning sample showed that the ability to generalize significantly decreases with increasing block size, which affects the quality of the synthesized image. Changing the degree of nonlinearity of synapses in the graphical user interface func*net Express, which was used for training and testing of the method suggests that the increase of this index does not significantly affect the perception of the image. The theoretical conclusions obtained by visual analysis of the synthesized images are complemented by the result ofevaluation metrics MSE, PSNR, UIQ and SSIM. Comparative analysis of the images enlarged 4 times, obtained by our method and two existing, shows best scores on all four metrics, suggesting the possibility of practical application of the method in a particular application area

Moving Objects Tracking in Real TIME Videostreams

This article describes a new method for tracking moving objects in the field of multiple cameras. The main feature of the proposed method is the ability to work in real time by significantly reducing procedural complexity. Based on this method, authors developed system to identify and support transport traffic. The results of practical experiments on the system show high accuracy of identification of moving objects, building the exact trajectory of their movement and possibility of them accompanied. Numerous practical experiments confirmed the efficiency of the proposed method in video surveillance systems with up to 8 cameras

Ensemble-based Method of Fraud Detection at Self-checkouts in Retail

The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use

Development of Machine Learning Method of Titanium Alloy Properties Identification in Additive Technologies

Based on the experimentally established data on the parameters of microstructure, elemental and fractional composition of titanium alloy powders, four classes of their conformity (a material with excellent properties, optimal properties, possible defects in the material and defective material) as source raw materials for the additive technologies are identified. The basic characteristics of the material, which determine its belonging to a certain class, are established. Training and test samples based on 20 features that characterize each of the four classes of titanium alloy powders for the implementation of machine learning procedures were built. The developed method for identification of the class of material, based on the use of the second-order Kolmogorov-Gabor polynomial and the Random Forest algorithm, is described. An experimental comparison of the developed method work results with existing methods: Random Forest, Logistic Regression, and Support Vectors Machines based on the accuracy of their work in the training and application modes was made. The visualization of the results of all the investigated methods was given.The developed supervised learning method allows constructing models for processing a large number of each input vector characteristics. In this case, the Random Forest algorithm provides satisfactory generalization properties while retaining the advantages of an additional increase of the accuracy based on the Kolmogorov-Gabor polynomial.The main advantages of the developed method, in particular, regarding the additional increase of the accuracy of the classification task solution, are experimentally determined. The developed method allows increasing the modeling accuracy by 34.38, 33.34 and 3.13 % compared with the methods: Support Vectors Machine, Logistic Regression, and Random Forest respectively.The obtained results allow one to considerably reduce financial and time expenses during the manufacture of products by additive technologies methods. The use of artificial intelligence tools can reduce the complexity and energy efficiency of experiments to determine the optimum characteristics of powder materials